Overview

Dataset statistics

Number of variables13
Number of observations174
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.8 KiB
Average record size in memory104.7 B

Variable types

Numeric9
Text3
Categorical1

Alerts

place is highly overall correlated with swim_time and 5 other fieldsHigh correlation
swim_time is highly overall correlated with place and 4 other fieldsHigh correlation
t1_time is highly overall correlated with place and 5 other fieldsHigh correlation
bike_time is highly overall correlated with place and 5 other fieldsHigh correlation
t2_time is highly overall correlated with place and 4 other fieldsHigh correlation
run_time is highly overall correlated with place and 5 other fieldsHigh correlation
chip_time is highly overall correlated with place and 5 other fieldsHigh correlation
place is uniformly distributedUniform
place has unique valuesUnique
bib has unique valuesUnique

Reproduction

Analysis started2023-08-19 22:34:18.505866
Analysis finished2023-08-19 22:34:29.289476
Duration10.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

place
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct174
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.5
Minimum1
Maximum174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-19T16:34:29.413135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.65
Q144.25
median87.5
Q3130.75
95-th percentile165.35
Maximum174
Range173
Interquartile range (IQR)86.5

Descriptive statistics

Standard deviation50.373604
Coefficient of variation (CV)0.57569833
Kurtosis-1.2
Mean87.5
Median Absolute Deviation (MAD)43.5
Skewness0
Sum15225
Variance2537.5
MonotonicityStrictly increasing
2023-08-19T16:34:29.610965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
120 1
 
0.6%
112 1
 
0.6%
113 1
 
0.6%
114 1
 
0.6%
115 1
 
0.6%
116 1
 
0.6%
117 1
 
0.6%
118 1
 
0.6%
119 1
 
0.6%
Other values (164) 164
94.3%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
174 1
0.6%
173 1
0.6%
172 1
0.6%
171 1
0.6%
170 1
0.6%
169 1
0.6%
168 1
0.6%
167 1
0.6%
166 1
0.6%
165 1
0.6%

bib
Real number (ℝ)

UNIQUE 

Distinct174
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean227.94253
Minimum120
Maximum328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-19T16:34:29.806263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile131.65
Q1176.25
median232.5
Q3279.75
95-th percentile318.7
Maximum328
Range208
Interquartile range (IQR)103.5

Descriptive statistics

Standard deviation60.52494
Coefficient of variation (CV)0.26552719
Kurtosis-1.1944077
Mean227.94253
Median Absolute Deviation (MAD)52
Skewness-0.092737574
Sum39662
Variance3663.2684
MonotonicityNot monotonic
2023-08-19T16:34:29.990414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
279 1
 
0.6%
152 1
 
0.6%
307 1
 
0.6%
139 1
 
0.6%
166 1
 
0.6%
328 1
 
0.6%
209 1
 
0.6%
197 1
 
0.6%
290 1
 
0.6%
188 1
 
0.6%
Other values (164) 164
94.3%
ValueCountFrequency (%)
120 1
0.6%
122 1
0.6%
123 1
0.6%
124 1
0.6%
126 1
0.6%
127 1
0.6%
129 1
0.6%
130 1
0.6%
131 1
0.6%
132 1
0.6%
ValueCountFrequency (%)
328 1
0.6%
327 1
0.6%
326 1
0.6%
325 1
0.6%
324 1
0.6%
323 1
0.6%
322 1
0.6%
321 1
0.6%
320 1
0.6%
318 1
0.6%
Distinct144
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-08-19T16:34:30.298880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.1436782
Min length2

Characters and Unicode

Total characters895
Distinct characters48
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)69.5%

Sample

1st rowPorter
2nd rowDwayne
3rd rowNathan
4th rowBen
5th rowDylan
ValueCountFrequency (%)
zachary 3
 
1.7%
ryan 3
 
1.7%
adam 3
 
1.7%
david 3
 
1.7%
mark 3
 
1.7%
alex 3
 
1.7%
doug 3
 
1.7%
mike 3
 
1.7%
thomas 2
 
1.1%
scott 2
 
1.1%
Other values (133) 147
84.0%
2023-08-19T16:34:30.767756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 114
 
12.7%
e 76
 
8.5%
i 62
 
6.9%
r 62
 
6.9%
n 61
 
6.8%
o 45
 
5.0%
l 41
 
4.6%
t 40
 
4.5%
h 37
 
4.1%
y 27
 
3.0%
Other values (38) 330
36.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 707
79.0%
Uppercase Letter 184
 
20.6%
Other Punctuation 3
 
0.3%
Space Separator 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 114
16.1%
e 76
10.7%
i 62
8.8%
r 62
8.8%
n 61
8.6%
o 45
 
6.4%
l 41
 
5.8%
t 40
 
5.7%
h 37
 
5.2%
y 27
 
3.8%
Other values (13) 142
20.1%
Uppercase Letter
ValueCountFrequency (%)
J 17
 
9.2%
M 17
 
9.2%
K 16
 
8.7%
D 15
 
8.2%
S 14
 
7.6%
A 14
 
7.6%
R 14
 
7.6%
T 13
 
7.1%
C 12
 
6.5%
L 7
 
3.8%
Other values (13) 45
24.5%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 891
99.6%
Common 4
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 114
 
12.8%
e 76
 
8.5%
i 62
 
7.0%
r 62
 
7.0%
n 61
 
6.8%
o 45
 
5.1%
l 41
 
4.6%
t 40
 
4.5%
h 37
 
4.2%
y 27
 
3.0%
Other values (36) 326
36.6%
Common
ValueCountFrequency (%)
. 3
75.0%
1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 895
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 114
 
12.7%
e 76
 
8.5%
i 62
 
6.9%
r 62
 
6.9%
n 61
 
6.8%
o 45
 
5.0%
l 41
 
4.6%
t 40
 
4.5%
h 37
 
4.1%
y 27
 
3.0%
Other values (38) 330
36.9%
Distinct162
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-08-19T16:34:31.038160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length6.7528736
Min length3

Characters and Unicode

Total characters1175
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)86.8%

Sample

1st rowMiddaugh
2nd rowDixon
3rd rowDutton
4th rowHogan
5th rowFriday
ValueCountFrequency (%)
lee 3
 
1.7%
gerber 2
 
1.1%
schuth 2
 
1.1%
allred 2
 
1.1%
geddes 2
 
1.1%
adams 2
 
1.1%
peterson 2
 
1.1%
cooper 2
 
1.1%
pritchardthorpe 2
 
1.1%
hogan 2
 
1.1%
Other values (157) 159
88.3%
2023-08-19T16:34:31.491015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 125
 
10.6%
n 100
 
8.5%
a 98
 
8.3%
r 94
 
8.0%
o 86
 
7.3%
i 60
 
5.1%
l 58
 
4.9%
s 49
 
4.2%
t 47
 
4.0%
d 36
 
3.1%
Other values (43) 422
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 957
81.4%
Uppercase Letter 205
 
17.4%
Space Separator 6
 
0.5%
Other Punctuation 5
 
0.4%
Dash Punctuation 2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 125
13.1%
n 100
10.4%
a 98
10.2%
r 94
9.8%
o 86
9.0%
i 60
 
6.3%
l 58
 
6.1%
s 49
 
5.1%
t 47
 
4.9%
d 36
 
3.8%
Other values (14) 204
21.3%
Uppercase Letter
ValueCountFrequency (%)
M 21
 
10.2%
S 21
 
10.2%
G 13
 
6.3%
P 13
 
6.3%
B 13
 
6.3%
C 12
 
5.9%
A 12
 
5.9%
D 11
 
5.4%
R 10
 
4.9%
O 10
 
4.9%
Other values (14) 69
33.7%
Other Punctuation
ValueCountFrequency (%)
. 3
60.0%
, 1
 
20.0%
' 1
 
20.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1162
98.9%
Common 13
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 125
 
10.8%
n 100
 
8.6%
a 98
 
8.4%
r 94
 
8.1%
o 86
 
7.4%
i 60
 
5.2%
l 58
 
5.0%
s 49
 
4.2%
t 47
 
4.0%
d 36
 
3.1%
Other values (38) 409
35.2%
Common
ValueCountFrequency (%)
6
46.2%
. 3
23.1%
- 2
 
15.4%
, 1
 
7.7%
' 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 125
 
10.6%
n 100
 
8.5%
a 98
 
8.3%
r 94
 
8.0%
o 86
 
7.3%
i 60
 
5.1%
l 58
 
4.9%
s 49
 
4.2%
t 47
 
4.0%
d 36
 
3.1%
Other values (43) 422
35.9%

gender
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
M
123 
F
51 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters174
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 123
70.7%
F 51
29.3%

Length

2023-08-19T16:34:31.668438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-19T16:34:31.842014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 123
70.7%
f 51
29.3%

Most occurring characters

ValueCountFrequency (%)
M 123
70.7%
F 51
29.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 174
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 123
70.7%
F 51
29.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 174
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 123
70.7%
F 51
29.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 123
70.7%
F 51
29.3%

age
Real number (ℝ)

Distinct52
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.045977
Minimum16
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-19T16:34:31.999013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile21
Q132
median40
Q351.75
95-th percentile64.35
Maximum73
Range57
Interquartile range (IQR)19.75

Descriptive statistics

Standard deviation12.935061
Coefficient of variation (CV)0.30764086
Kurtosis-0.57181614
Mean42.045977
Median Absolute Deviation (MAD)10
Skewness0.18756451
Sum7316
Variance167.31579
MonotonicityNot monotonic
2023-08-19T16:34:32.191496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 11
 
6.3%
39 8
 
4.6%
31 7
 
4.0%
50 7
 
4.0%
60 7
 
4.0%
38 6
 
3.4%
37 6
 
3.4%
47 5
 
2.9%
44 5
 
2.9%
36 5
 
2.9%
Other values (42) 107
61.5%
ValueCountFrequency (%)
16 2
1.1%
17 1
 
0.6%
19 1
 
0.6%
20 3
1.7%
21 4
2.3%
22 1
 
0.6%
23 2
1.1%
25 4
2.3%
26 2
1.1%
27 3
1.7%
ValueCountFrequency (%)
73 1
0.6%
72 1
0.6%
71 1
0.6%
70 1
0.6%
68 1
0.6%
66 2
1.1%
65 2
1.1%
64 1
0.6%
62 1
0.6%
61 2
1.1%

city
Text

Distinct123
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-08-19T16:34:32.478060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length8.4425287
Min length4

Characters and Unicode

Total characters1469
Distinct characters50
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)57.5%

Sample

1st rowVail
2nd rowRALEIGH
3rd rowHighland
4th rowLouisville
5th rowEdwards
ValueCountFrequency (%)
edwards 10
 
4.6%
spgs 10
 
4.6%
colorado 9
 
4.1%
boulder 9
 
4.1%
denver 9
 
4.1%
golden 6
 
2.7%
vail 6
 
2.7%
louisville 5
 
2.3%
durango 5
 
2.3%
avon 5
 
2.3%
Other values (117) 145
66.2%
2023-08-19T16:34:32.949254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 95
 
6.5%
a 87
 
5.9%
o 79
 
5.4%
n 74
 
5.0%
l 65
 
4.4%
L 62
 
4.2%
r 58
 
3.9%
A 55
 
3.7%
E 55
 
3.7%
O 52
 
3.5%
Other values (40) 787
53.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 810
55.1%
Uppercase Letter 614
41.8%
Space Separator 45
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 95
11.7%
a 87
10.7%
o 79
9.8%
n 74
9.1%
l 65
 
8.0%
r 58
 
7.2%
d 51
 
6.3%
s 46
 
5.7%
i 46
 
5.7%
t 45
 
5.6%
Other values (16) 164
20.2%
Uppercase Letter
ValueCountFrequency (%)
L 62
 
10.1%
A 55
 
9.0%
E 55
 
9.0%
O 52
 
8.5%
S 50
 
8.1%
D 47
 
7.7%
R 46
 
7.5%
C 31
 
5.0%
G 31
 
5.0%
N 27
 
4.4%
Other values (13) 158
25.7%
Space Separator
ValueCountFrequency (%)
45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1424
96.9%
Common 45
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 95
 
6.7%
a 87
 
6.1%
o 79
 
5.5%
n 74
 
5.2%
l 65
 
4.6%
L 62
 
4.4%
r 58
 
4.1%
A 55
 
3.9%
E 55
 
3.9%
O 52
 
3.7%
Other values (39) 742
52.1%
Common
ValueCountFrequency (%)
45
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 95
 
6.5%
a 87
 
5.9%
o 79
 
5.4%
n 74
 
5.0%
l 65
 
4.4%
L 62
 
4.2%
r 58
 
3.9%
A 55
 
3.7%
E 55
 
3.7%
O 52
 
3.5%
Other values (40) 787
53.6%

swim_time
Real number (ℝ)

HIGH CORRELATION 

Distinct156
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1639.6667
Minimum1041
Maximum3288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-19T16:34:33.145642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1041
5-th percentile1212.65
Q11421.5
median1557
Q31773
95-th percentile2246.1
Maximum3288
Range2247
Interquartile range (IQR)351.5

Descriptive statistics

Standard deviation357.8849
Coefficient of variation (CV)0.21826686
Kurtosis3.8058492
Mean1639.6667
Median Absolute Deviation (MAD)172.5
Skewness1.5876971
Sum285302
Variance128081.6
MonotonicityNot monotonic
2023-08-19T16:34:33.334579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1464 4
 
2.3%
1760 2
 
1.1%
1421 2
 
1.1%
1583 2
 
1.1%
1525 2
 
1.1%
1429 2
 
1.1%
1548 2
 
1.1%
1905 2
 
1.1%
1439 2
 
1.1%
1647 2
 
1.1%
Other values (146) 152
87.4%
ValueCountFrequency (%)
1041 1
0.6%
1112 1
0.6%
1119 1
0.6%
1150 1
0.6%
1154 1
0.6%
1171 1
0.6%
1174 1
0.6%
1185 1
0.6%
1212 1
0.6%
1213 1
0.6%
ValueCountFrequency (%)
3288 1
0.6%
2952 1
0.6%
2825 1
0.6%
2711 1
0.6%
2687 1
0.6%
2666 1
0.6%
2459 1
0.6%
2439 1
0.6%
2302 1
0.6%
2216 1
0.6%

t1_time
Real number (ℝ)

HIGH CORRELATION 

Distinct127
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean264.63793
Minimum121
Maximum600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-19T16:34:33.522752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum121
5-th percentile159.65
Q1210.25
median247.5
Q3303.5
95-th percentile410.3
Maximum600
Range479
Interquartile range (IQR)93.25

Descriptive statistics

Standard deviation83.633808
Coefficient of variation (CV)0.31603107
Kurtosis2.2522342
Mean264.63793
Median Absolute Deviation (MAD)45
Skewness1.2596125
Sum46047
Variance6994.6138
MonotonicityNot monotonic
2023-08-19T16:34:33.728192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
215 4
 
2.3%
264 3
 
1.7%
271 3
 
1.7%
226 3
 
1.7%
242 3
 
1.7%
260 3
 
1.7%
321 3
 
1.7%
279 3
 
1.7%
192 3
 
1.7%
240 3
 
1.7%
Other values (117) 143
82.2%
ValueCountFrequency (%)
121 1
0.6%
124 1
0.6%
141 1
0.6%
149 1
0.6%
151 1
0.6%
152 1
0.6%
153 1
0.6%
159 2
1.1%
160 1
0.6%
166 1
0.6%
ValueCountFrequency (%)
600 1
0.6%
584 1
0.6%
524 1
0.6%
496 1
0.6%
485 1
0.6%
460 1
0.6%
440 1
0.6%
426 1
0.6%
422 1
0.6%
404 1
0.6%

bike_time
Real number (ℝ)

HIGH CORRELATION 

Distinct170
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7179.069
Minimum5002
Maximum10903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-19T16:34:34.018767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5002
5-th percentile5555.5
Q16426.5
median6973.5
Q37828.5
95-th percentile9471.2
Maximum10903
Range5901
Interquartile range (IQR)1402

Descriptive statistics

Standard deviation1194.009
Coefficient of variation (CV)0.16631808
Kurtosis0.65306025
Mean7179.069
Median Absolute Deviation (MAD)723
Skewness0.81665013
Sum1249158
Variance1425657.5
MonotonicityNot monotonic
2023-08-19T16:34:34.211530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6594 2
 
1.1%
6498 2
 
1.1%
8117 2
 
1.1%
7634 2
 
1.1%
8009 1
 
0.6%
7380 1
 
0.6%
7439 1
 
0.6%
7811 1
 
0.6%
7361 1
 
0.6%
7595 1
 
0.6%
Other values (160) 160
92.0%
ValueCountFrequency (%)
5002 1
0.6%
5160 1
0.6%
5222 1
0.6%
5281 1
0.6%
5348 1
0.6%
5400 1
0.6%
5414 1
0.6%
5491 1
0.6%
5549 1
0.6%
5559 1
0.6%
ValueCountFrequency (%)
10903 1
0.6%
10833 1
0.6%
10529 1
0.6%
10465 1
0.6%
10200 1
0.6%
10052 1
0.6%
9920 1
0.6%
9665 1
0.6%
9544 1
0.6%
9432 1
0.6%

t2_time
Real number (ℝ)

HIGH CORRELATION 

Distinct114
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.27011
Minimum37
Maximum398
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-19T16:34:34.405173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile47
Q177
median101.5
Q3139.5
95-th percentile248
Maximum398
Range361
Interquartile range (IQR)62.5

Descriptive statistics

Standard deviation65.91518
Coefficient of variation (CV)0.55732744
Kurtosis4.0919952
Mean118.27011
Median Absolute Deviation (MAD)32
Skewness1.8263337
Sum20579
Variance4344.811
MonotonicityNot monotonic
2023-08-19T16:34:34.610566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 5
 
2.9%
61 4
 
2.3%
85 4
 
2.3%
84 4
 
2.3%
114 3
 
1.7%
109 3
 
1.7%
68 3
 
1.7%
55 3
 
1.7%
94 3
 
1.7%
103 3
 
1.7%
Other values (104) 139
79.9%
ValueCountFrequency (%)
37 1
0.6%
38 1
0.6%
39 1
0.6%
42 1
0.6%
43 1
0.6%
45 1
0.6%
46 2
1.1%
47 2
1.1%
48 2
1.1%
49 1
0.6%
ValueCountFrequency (%)
398 1
0.6%
363 2
1.1%
330 1
0.6%
329 1
0.6%
313 1
0.6%
295 1
0.6%
269 1
0.6%
248 2
1.1%
241 1
0.6%
236 1
0.6%

run_time
Real number (ℝ)

HIGH CORRELATION 

Distinct170
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4074.2471
Minimum2598
Maximum6409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-19T16:34:34.810244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2598
5-th percentile2953.85
Q13504
median3927
Q34483.25
95-th percentile5638.85
Maximum6409
Range3811
Interquartile range (IQR)979.25

Descriptive statistics

Standard deviation805.26223
Coefficient of variation (CV)0.19764688
Kurtosis0.18707796
Mean4074.2471
Median Absolute Deviation (MAD)482.5
Skewness0.76974109
Sum708919
Variance648447.26
MonotonicityNot monotonic
2023-08-19T16:34:35.014051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3434 2
 
1.1%
3619 2
 
1.1%
4471 2
 
1.1%
3502 2
 
1.1%
2598 1
 
0.6%
4486 1
 
0.6%
3963 1
 
0.6%
3989 1
 
0.6%
3932 1
 
0.6%
4219 1
 
0.6%
Other values (160) 160
92.0%
ValueCountFrequency (%)
2598 1
0.6%
2669 1
0.6%
2798 1
0.6%
2866 1
0.6%
2878 1
0.6%
2916 1
0.6%
2919 1
0.6%
2942 1
0.6%
2948 1
0.6%
2957 1
0.6%
ValueCountFrequency (%)
6409 1
0.6%
6221 1
0.6%
6152 1
0.6%
6119 1
0.6%
6114 1
0.6%
5878 1
0.6%
5847 1
0.6%
5820 1
0.6%
5776 1
0.6%
5565 1
0.6%

chip_time
Real number (ℝ)

HIGH CORRELATION 

Distinct170
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13275.351
Minimum9307
Maximum19853
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-08-19T16:34:35.220327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9307
5-th percentile10059.05
Q111746.5
median12791
Q314733.25
95-th percentile17420.3
Maximum19853
Range10546
Interquartile range (IQR)2986.75

Descriptive statistics

Standard deviation2232.172
Coefficient of variation (CV)0.16814411
Kurtosis0.3381533
Mean13275.351
Median Absolute Deviation (MAD)1434
Skewness0.74549315
Sum2309911
Variance4982591.9
MonotonicityIncreasing
2023-08-19T16:34:35.422170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14780 2
 
1.1%
11698 2
 
1.1%
14850 2
 
1.1%
13028 2
 
1.1%
14197 1
 
0.6%
13896 1
 
0.6%
13917 1
 
0.6%
13968 1
 
0.6%
13987 1
 
0.6%
14029 1
 
0.6%
Other values (160) 160
92.0%
ValueCountFrequency (%)
9307 1
0.6%
9402 1
0.6%
9459 1
0.6%
9726 1
0.6%
9906 1
0.6%
9989 1
0.6%
10001 1
0.6%
10017 1
0.6%
10035 1
0.6%
10072 1
0.6%
ValueCountFrequency (%)
19853 1
0.6%
19453 1
0.6%
19430 1
0.6%
19152 1
0.6%
19126 1
0.6%
18667 1
0.6%
18427 1
0.6%
17889 1
0.6%
17562 1
0.6%
17344 1
0.6%

Interactions

2023-08-19T16:34:27.753222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:19.547570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:20.645849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:21.592417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:22.668833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:23.636193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:24.623652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:25.652144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:26.697638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:27.868523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:19.723107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:20.756080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:21.709304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:22.783199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:23.751652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:24.744117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:25.777489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:26.820816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:28.079484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:19.826973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:20.849494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:21.805842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:22.877591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:23.849504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:24.844842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:25.881250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:26.927278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:28.187720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:19.939846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:20.950593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:21.912807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:22.983128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:23.955807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:24.956543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:25.994763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:27.041469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:28.291703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:20.048812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:21.048568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:22.015805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:23.081098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:24.059134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:25.062207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:26.102082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:27.151324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:28.398879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:20.163481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:21.150301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:22.123039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:23.187109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:24.164610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:25.173173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:26.221442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:27.267112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:28.511189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:20.283830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:21.261058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:22.323012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:23.297159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:24.278347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:25.289029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:26.337898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:27.385921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:28.627149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:20.406037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:21.371786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:22.438552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:23.411895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:24.393784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:25.411963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:26.457727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:27.510976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:28.748311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:20.532495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:21.489935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:22.560420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:23.529896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:24.516837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:25.541457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:26.585103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-19T16:34:27.637593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-19T16:34:35.555385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
placebibageswim_timet1_timebike_timet2_timerun_timechip_timegender
place1.000-0.1500.2140.6770.7430.9490.7470.9291.0000.224
bib-0.1501.000-0.0790.009-0.069-0.171-0.162-0.165-0.1500.070
age0.214-0.0791.0000.2260.1640.1420.1520.2550.2140.045
swim_time0.6770.0090.2261.0000.5960.5280.4370.5890.6770.137
t1_time0.743-0.0690.1640.5961.0000.6760.7090.6550.7430.070
bike_time0.949-0.1710.1420.5280.6761.0000.6870.8120.9490.263
t2_time0.747-0.1620.1520.4370.7090.6871.0000.7060.7470.113
run_time0.929-0.1650.2550.5890.6550.8120.7061.0000.9290.092
chip_time1.000-0.1500.2140.6770.7430.9490.7470.9291.0000.197
gender0.2240.0700.0450.1370.0700.2630.1130.0920.1971.000

Missing values

2023-08-19T16:34:28.911933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-19T16:34:29.172427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

placebibfirst_namelast_namegenderagecityswim_timet1_timebike_timet2_timerun_timechip_time
01279PorterMiddaughM17Vail132712152223925989307
12298DwayneDixonM37RALEIGH117414951605428669402
23267NathanDuttonM16Highland104115950028631719459
34313BenHoganM25Louisville128716054146827989726
45145DylanFridayM29Edwards130715257423726699906
56303ScottMcCalmonM43BOULDER141725852817629579989
67318HoseaShepherdM38Sonora1303172555952291610001
78288ThomasSpannringM46Longmont1526153534843294810017
89264MichaelDorrM47AVON1402171540057300410035
910242DrewKroekerM21BOULDER1112151575446301010072
placebibfirst_namelast_namegenderagecityswim_timet1_timebike_timet2_timerun_timechip_time
164165289KimWagnerF60TEMPE28253998615236527017344
165166282DAVESUTTONM57Louisville20613969544176538617562
166167248MattRadloffM30Jacksonville152358410200248533417889
167168246JoGaruccioF71Sandy17493839920154622118427
168169154StormieWellsF48Colorado Spgs211340410465145554118667
169170224SheriSchrockF68Cohasset245935410052110615219126
170171240JohnStacyM50Catlettsburg32885249665156551919152
171172234CarlosCastroM27Avon268742610833143534119430
172173287ChadCarpenterM39AVON266649610529398536519453
173174149MelissaBriggsF45DURANGO183244010903269640919853